AUTHOR=Liang Zihong , Chen Junjie , Xu Zhaopeng , Chen Yuyang , Hao Tianyong 

TITLE=A Pattern-Based Method for Medical Entity Recognition From Chinese Diagnostic Imaging Text

JOURNAL=Frontiers in Artificial Intelligence

VOLUME=Volume 2 - 2019

YEAR=2019

URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2019.00001

DOI=10.3389/frai.2019.00001

ISSN=2624-8212

ABSTRACT=Background: The identification of medical entities and relations from electronic medical records is a
fundamental research for medical informatics. However, extracting valuable knowledge from these records
is a challenging work due to its high complexity. The accurate identification of entity and relation is still
an open research problem in medical information extraction.
Methods: A pattern-based method for extracting certain tumor related entities and attributes from
Chinese unstructured diagnostic imaging text is proposed. This method is a composition of three steps.
Firstly, an algorithm based on keyword matching is designed to obtain the primary sites of tumors. Then
a set of regular expressions is applied to identify primary tumor size information. Finally, a set of rules
is defined to acquire metastatic sites of tumors.
Results: Our method achieves a recall of 0.697, a precision of 0.825 and an F1 score of 0.755 using
an overall weighted metric. For each of extraction tasks, the F1 scores are 0.784, 0.822 and 0.740,
respectively.
Conclusions: The method is proved to be stable and robust with different amounts of testing data.
It achieves a comparatively high performance in CHIP 2018 open challenge, demonstrating its
effectiveness in extracting tumor related entities from Chinese diagnostic imaging text.